Mitochondria are specialized eukaryotic organelles that have a dedicated function in oxygen respiration and energy production. They evolved about 2 billion years ago from a free-living bacterial ancestor (probably an alphaproteobacterium), in a process known as endosymbiosis(1,2). Many unicellular eukaryotes have since adapted to life in anoxic habitats and their mitochondria have undergone further reductive evolution(3). As a result, obligate anaerobic eukaryotes with mitochondrial remnants derive their energy mostly from fermentation4. Here we describe 'Candidatus Azoamicus ciliaticola', which is an obligate endosymbiont of an anaerobic ciliate and has a dedicated role in respiration and providing energy for its eukaryotic host. 'Candidatus A. ciliaticola' contains a highly reduced 0.29-Mb genome that encodes core genes for central information processing, the electron transport chain, a truncated tricarboxylic acid cycle, ATP generation and iron-sulfur cluster biosynthesis. The genome encodes a respiratory denitrification pathway instead of aerobic terminal oxidases, which enables its host to breathe nitrate instead of oxygen. 'Candidatus A. ciliaticola' and its ciliate host represent an example of a symbiosis that is based on the transfer of energy in the form of ATP, rather than nutrition. This discovery raises the possibility that eukaryotes with mitochondrial remnants may secondarily acquire energy-providing endosymbionts to complement or replace functions of their mitochondria.

Emissions of ozone-depleting substances, including trichlorofluoromethane (CFC-11), have decreased since the mid-1980s in response to the Montreal Protocol(1,2). In recent years, an unexpected increase in CFC-11 emissions beginning in 2013 has been reported, with much of the global rise attributed to emissions from eastern China(3,4). Here we use high-frequency atmospheric mole fraction observations from Gosan, South Korea and Hateruma, Japan, together with atmospheric chemical transport-model simulations, to investigate regional CFC-11 emissions from eastern China. We find that CFC-11 emissions returned to pre-2013 levels in 2019 (5.0 +/- 1.0 gigagrams per year in 2019, compared to 7.2 +/- 1.5 gigagrams per year for 2008-2012, +/- 1 standard deviation), decreasing by 10 +/- 3 gigagrams per year since 2014-2017. Furthermore, we find that in this region, carbon tetrachloride (CCl4) and dichlorodifluoromethane (CFC-12) emissions-potentially associated with CFC-11 production-were higher than expected after 2013 and then declined one to two years before the CFC-11 emissions reduction. This suggests that CFC-11 production occurred in eastern China after the mandated global phase-out, and that there was a subsequent decline in production during 2017-2018. We estimate that the amount of the CFC-11 bank (the amount of CFC-11 produced, but not yet emitted) in eastern China is up to 112 gigagrams larger in 2019 compared to pre-2013 levels, probably as a result of recent production. Nevertheless, it seems that any substantial delay in ozone-layer recovery has been avoided, perhaps owing to timely reporting(3,4) and subsequent action by industry and government in China(5,6). Atmospheric data and chemical-transport modelling show that CFC-11 emissions from eastern China have again decreased, after increasing in 2013-2017, and a delay in ozone-layer recovery has probably been avoided.

Nucleotide-binding domain, leucine-rich repeat receptors (NLRs) mediate innate immunity by forming inflammasomes. Activation of the NLR protein NLRP1 requires autocleavage within its function-to-find domain (FIIND)(1-7). In resting cells, the dipeptidyl peptidases DPP8 and DPP9 interact with the FIIND of NLRP1 and suppress spontaneous NLRP1 activation(8,9); however, the mechanisms through which this occurs remain unknown. Here we present structural and biochemical evidence that full-length rat NLRP1 (rNLRP1) and rat DPP9 (rDPP9) form a 2:1 complex that contains an autoinhibited rNLRP1 molecule and an active UPA-CARD fragment of rNLRP1. The ZU5 domain is required not only for autoinhibition of rNLRP1 but also for assembly of the 2:1 complex. Formation of the complex prevents UPA-mediated higher-order oligomerization of UPA-CARD fragments and strengthens ZU5-mediated NLRP1 autoinhibition. Structure-guided biochemical and functional assays show that both NLRP1 binding and enzymatic activity are required for DPP9 to suppress NLRP1 in human cells. Together, our data reveal the mechanism of DPP9-mediated inhibition of NLRP1 and shed light on the activation of the NLRP1 inflammasome.

Suppressing infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will probably require the rapid identification and isolation of individuals infected with the virus on an ongoing basis. Reverse-transcription polymerase chain reaction (RT-PCR) tests are accurate but costly, which makes the regular testing of every individual expensive. These costs are a challenge for all countries around the world, but particularly for low-to-middle-income countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups(1-7). A balance must be struck between increasing the group size and retaining test sensitivity, as sample dilution increases the likelihood of false-negative test results for individuals with a low viral load in the sampled region at the time of the test(8). Similarly, minimizing the number of tests to reduce costs must be balanced against minimizing the time that testing takes, to reduce the spread of the infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof-of-concept experiments in which a positive subsample was detected even when diluted 100-fold with negative subsamples (compared with 30-48-fold dilutions described in previous studies(9-11)). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by the frequent re-testing of groups, for example. With the use of these methods, the cost of mass testing could be reduced by a large factor. At low prevalence, the costs decrease in rough proportion to the prevalence. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to monitor infection rates closely and continually in a population, along with the rapid and effective isolation of people with SARS-CoV-2 infections, provides a promising pathway towards the long-term control of coronavirus disease 2019 (COVID-19). A mathematical algorithm for population-wide screening of SARS-CoV-2 infections using pooled parallel RT-PCR tests requires considerably fewer tests than individual testing procedures and has minimal delays in the identification of individuals infected with SARS-CoV-2.

There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging(1-3). Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.

COVID-19 manifests with a wide spectrum of clinical phenotypes that are characterized by exaggerated and misdirected host immune responses(1-6). Although pathological innate immune activation is well-documented in severe disease(1), the effect of autoantibodies on disease progression is less well-defined. Here we use a high-throughput autoantibody discovery technique known as rapid extracellular antigen profiling(7) to screen a cohort of 194 individuals infected with SARS-CoV-2, comprising 172 patients with COVID-19 and 22 healthcare workers with mild disease or asymptomatic infection, for autoantibodies against 2,770 extracellular and secreted proteins (members of the exoproteome). We found that patients with COVID-19 exhibit marked increases in autoantibody reactivities as compared to uninfected individuals, and show a high prevalence of autoantibodies against immunomodulatory proteins (including cytokines, chemokines, complement components and cell-surface proteins). We established that these autoantibodies perturb immune function and impair virological control by inhibiting immunoreceptor signalling and by altering peripheral immune cell composition, and found that mouse surrogates of these autoantibodies increase disease severity in a mouse model of SARS-CoV-2 infection. Our analysis of autoantibodies against tissue-associated antigens revealed associations with specific clinical characteristics. Our findings suggest a pathological role for exoproteome-directed autoantibodies in COVID-19, with diverse effects on immune functionality and associations with clinical outcomes.